In recent years, the world has witnessed explosive growth in the demand for wireless communications and it is predicted that this demand will increase in the future. There are already over 500 million users that subscribe to cellular telephone services and the number is continually increasing. Eventually, in the not too distant future the number of cellular subscribers will exceed the number of fixed line telephone installations. Already, in many cases, the revenues from mobile services already exceeds that for fixed line services even though the amount of traffic generated through mobile phones is much less than in fixed networks.
Other related wireless technologies have experienced growth similar to that of cellular. For example, cordless telephony, two way radio trunking systems, paging (one way and two way), messaging, wireless local area networks (WLANs) and wireless local loops (WLLs). In addition, new broadband communication schemes are rapidly being deployed to provide users with increased bandwidth and faster access to the Internet. Broadband services such as xDSL, short range high speed wireless connections, high rate satellite downlink (and the uplink in some cases) are being offered to users in more and more locations.
In connection with cellular services, the majority of users currently subscribe to digital cellular networks. Almost all new cellular handsets sold to customers are based on digital technology, typically second generation digital technology. Currently, third generation digital networks are being designed and tested which will be able to support data packet networks and much higher data rates. The first generation analog systems comprise the well known protocols AMPS, TACS, etc. The digital systems comprise GSM, TDMA (IS-136) or CDMA (IS-95), for example.
A diagram illustrating an example prior art communication system employing an inner and outer encoder in the transmitter, inner and outer decoding stages in the receiver and a noise source after the channel is shown in FIG. 1. The communication system, generally referenced 10, represents the typical scheme that may be used in many of the communication services described above. In such as system, the transmitter 11 comprises an encoder 14, interleaver 15, symbol generator 16 and modulator 18. Input data bits 12 to be transmitted are input to the encoder 14 which may comprise an error correction encoder such as Reed Solomon, convolutional encoder, parity bit generator, etc. The encoder functions to add redundancy bits to enable errors in transmission to be located and fixed.
It is noted that both the inner and outer decoders in the receiver have complimentary encoders in the transmitter. The outer encoder in the transmitter comprises the encoder 14, e.g., Reed Solomon, etc. The inner encoder comprises the channel 20 which often times can be modeled as an L-symbol long FIR-type channel.
The bits output of the encoder are then interleaved wherein the order of the bits are changed so as to more efficiently combat error bursts. The rearrangement of the bits caused by interleaving improves the resistance to error bursts but add latency and delay to the transmission.
The bits output of the interleaver are then mapped to symbols by the symbol generator 16. The symbol generator functions to transform the bits to modulator symbols. For example, an 8-PSK modulator converts input bits into one of eight symbols. Thus, the symbol generator generates a symbol for every three input bits.
The output from the mapper is input to the modulator which receives symbols in the M-ary alphabet and generates the analog signal that is subsequently transmitted over the channel 20. The channel may comprise a mobile wireless channel, e.g., cellular, cordless, a fixed wireless channel, e.g., satellite, or may comprise a wired channel, e.g., xDSL, ISDN, Ethernet, etc. The processing performed in the transmitter is intended to generate a signal that can be transmitted over the channel so as to provide robust, error free detection by the receiver.
At the receiver 13, the analog signal from the channel is input to front end circuitry 22 which demodulates and samples the received signal to generate received samples y(k) 21. The samples are first input to an inner decoder 24. An example of an inner decoder is an equalizer which compensates for the ISI caused by the delay and time spreading of the channel in attempting to detect the symbols that were originally transmitted by the modulator.
Equalizers can be adapted to output hard symbol decisions or soft symbol decisions. Examples of commonly used types of hard decision equalizers include the maximum likelihood sequence estimation (MLSE) equalizer that utilize the well known Viterbi Algorithm (VA), linear equalizer and decision feedback equalizer (DFE). Examples of soft output type equalizers include Soft Output Viterbi Algorithm (SOVA) type equalizers and equalizers based on the Maximum A Posteriori (MAP) algorithm.
In the case of a hard output equalizer, the output of the inner decoder comprises symbols s(k) 23 which represent hard decisions. If a soft output decoder is used, the symbols s(k) output of the inner decoder comprise soft symbol decisions. The output of the inner decoder is then input to a symbol to bit de-mapper 25 which is adapted to map the received symbols to bits. In the case of 8-PSK, for example, each symbol is mapped to three bits.
The output of the symbol to bit mapper is then input to a bit de-interleaver 26 which functions to restore the original order of the bits. The bits are then input to an outer decoder 29 which functions to locate and fix errors using the redundancy inserted by the encoder. The outer decoder generates the binary receive data ak 28.
Examples of the outer decoder include turbo decoders and convolutional decoders that utilize the Viterbi Algorithm. This class of decoders provides better performance by taking into account soft information about the reliability of the received symbol. The improved performance of the decoder cannot be realized, however, when soft information about the received symbols is not available. Note that the Viterbi algorithm is widely used in communication systems and has been adapted to perform functions including demodulation, decoding, equalization, etc. Many systems utilize the Viterbi Algorithm in both the inner and outer decoding stages.
As described above, the outer decoder, in some systems, is adapted to utilize the symbol decisions output from the inner decoder, e.g., the equalizer. Optimal decoders, however, require soft decisions rather than hard decisions. For example, an outer decoder that utilizes the Viterbi Algorithm to perform convolutional forward error correction decoding, requires soft decisions as input. The advantage of a Viterbi decoder is that it can efficiently process soft decision information. In order to provide soft symbol decisions, the inner decoder typically comprises a soft output equalizer such as a SOVA or MAP based equalizer.
In some cases, such as with a system using a punctured code or bit wise rather then symbol wise interleaving, soft symbol decisions cannot be used. In such cases, optimal decoders require soft bit inputs rather than soft symbol inputs. Soft bit decisions may comprise a bit value and the reliability of the decision. Alternatively, a soft bit decision may comprise only the reliability value for a ‘1’ (or alternately ‘0’) decision.
Although the de-mapper maps the symbols into bits, there is no mechanism to convert the soft symbol decision information into soft bit decision information, which is needed by the soft outer decoder.
The problem is illustrated when considering a receiver adapted to handle a GSM Enhanced General Packet Radio System (EGPRS) signal. Such a system utilizes a bit wise interleaver and punctured convolutional coding for performing Forward Error Correction (FEC) over channels that require equalization. Assume that the equalizer employs a Soft Output Viterbi Algorithm in its operation and that the outer FEC decoder employs the Viterbi Algorithm. After de-interleaving, the soft symbol decision information output of the equalizer is no longer related to the bits output of the de-interleaver.
A prior art technique for generating soft bit decisions is described in “A Soft-Decision State-Space Equalizer for FIR Channels,” J. Thielecke, IEEE Transactions on Communications, Vol. 45, No. 10, October 1997. A nonlinear equalizer is described that is intended for FIR channels which is based on a state-space description of the channel. The algorithm utilizes equations that resemble a Kalman hard decision feedback equalizer whereby the probability estimates of the received bits are incorporated.
A disadvantage of this prior art technique is that the level of computational complexity is relatively high making implementation difficult in communication systems. In addition, the technique is restricted to a particular type of channel and a particular way of describing the channel.